Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue.
Identifieur interne : 000653 ( PubMed/Checkpoint ); précédent : 000652; suivant : 000654Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue.
Auteurs : Jared Ostmeyer [États-Unis] ; Scott Christley [États-Unis] ; Inimary T. Toby [États-Unis] ; Lindsay G. Cowell [États-Unis]Source :
- Cancer research [ 1538-7445 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Complementarity Determining Regions, Receptors, Antigen, T-Cell, alpha-beta.
- chemical , genetics : Receptors, Antigen, T-Cell.
- immunology : T-Lymphocytes.
- Humans, Lymphocytes, Tumor-Infiltrating.
Abstract
Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRβ chains. To develop each classifier, we extracted 4-mers from every TCRβ CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays. SIGNIFICANCE: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.See related commentary by Zoete and Coukos, p. 1299.
DOI: 10.1158/0008-5472.CAN-18-2292
PubMed: 30622114
Affiliations:
Links toward previous steps (curation, corpus...)
Links to Exploration step
pubmed:30622114Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue.</title><author><name sortKey="Ostmeyer, Jared" sort="Ostmeyer, Jared" uniqKey="Ostmeyer J" first="Jared" last="Ostmeyer">Jared Ostmeyer</name><affiliation wicri:level="2"><nlm:affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Texas</region></placeName><wicri:cityArea>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas</wicri:cityArea></affiliation></author><author><name sortKey="Christley, Scott" sort="Christley, Scott" uniqKey="Christley S" first="Scott" last="Christley">Scott Christley</name><affiliation wicri:level="2"><nlm:affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Texas</region></placeName><wicri:cityArea>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas</wicri:cityArea></affiliation></author><author><name sortKey="Toby, Inimary T" sort="Toby, Inimary T" uniqKey="Toby I" first="Inimary T" last="Toby">Inimary T. Toby</name><affiliation wicri:level="2"><nlm:affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Texas</region></placeName><wicri:cityArea>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas</wicri:cityArea></affiliation></author><author><name sortKey="Cowell, Lindsay G" sort="Cowell, Lindsay G" uniqKey="Cowell L" first="Lindsay G" last="Cowell">Lindsay G. Cowell</name><affiliation wicri:level="2"><nlm:affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas. lindsay.cowell@utsouthwestern.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Texas</region></placeName><wicri:cityArea>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas</wicri:cityArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2019">2019</date><idno type="RBID">pubmed:30622114</idno><idno type="pmid">30622114</idno><idno type="doi">10.1158/0008-5472.CAN-18-2292</idno><idno type="wicri:Area/PubMed/Corpus">000683</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000683</idno><idno type="wicri:Area/PubMed/Curation">000683</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000683</idno><idno type="wicri:Area/PubMed/Checkpoint">000653</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000653</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue.</title><author><name sortKey="Ostmeyer, Jared" sort="Ostmeyer, Jared" uniqKey="Ostmeyer J" first="Jared" last="Ostmeyer">Jared Ostmeyer</name><affiliation wicri:level="2"><nlm:affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Texas</region></placeName><wicri:cityArea>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas</wicri:cityArea></affiliation></author><author><name sortKey="Christley, Scott" sort="Christley, Scott" uniqKey="Christley S" first="Scott" last="Christley">Scott Christley</name><affiliation wicri:level="2"><nlm:affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Texas</region></placeName><wicri:cityArea>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas</wicri:cityArea></affiliation></author><author><name sortKey="Toby, Inimary T" sort="Toby, Inimary T" uniqKey="Toby I" first="Inimary T" last="Toby">Inimary T. Toby</name><affiliation wicri:level="2"><nlm:affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Texas</region></placeName><wicri:cityArea>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas</wicri:cityArea></affiliation></author><author><name sortKey="Cowell, Lindsay G" sort="Cowell, Lindsay G" uniqKey="Cowell L" first="Lindsay G" last="Cowell">Lindsay G. Cowell</name><affiliation wicri:level="2"><nlm:affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas. lindsay.cowell@utsouthwestern.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><placeName><region type="state">Texas</region></placeName><wicri:cityArea>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas</wicri:cityArea></affiliation></author></analytic><series><title level="j">Cancer research</title><idno type="eISSN">1538-7445</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Complementarity Determining Regions (chemistry)</term><term>Humans</term><term>Lymphocytes, Tumor-Infiltrating</term><term>Receptors, Antigen, T-Cell (genetics)</term><term>Receptors, Antigen, T-Cell, alpha-beta (chemistry)</term><term>T-Lymphocytes (immunology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Humains</term><term>Lymphocytes T (immunologie)</term><term>Lymphocytes TIL</term><term>Récepteur lymphocytaire T antigène, alpha-bêta ()</term><term>Récepteurs aux antigènes des cellules T (génétique)</term><term>Régions déterminant la complémentarité ()</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Complementarity Determining Regions</term><term>Receptors, Antigen, T-Cell, alpha-beta</term></keywords><keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Receptors, Antigen, T-Cell</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Récepteurs aux antigènes des cellules T</term></keywords><keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Lymphocytes T</term></keywords><keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>T-Lymphocytes</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term><term>Lymphocytes, Tumor-Infiltrating</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Lymphocytes TIL</term><term>Récepteur lymphocytaire T antigène, alpha-bêta</term><term>Régions déterminant la complémentarité</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRβ chains. To develop each classifier, we extracted 4-mers from every TCRβ CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays. SIGNIFICANCE: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.<i>See related commentary by Zoete and Coukos, p. 1299</i>.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Automated" Owner="NLM"><PMID Version="1">30622114</PMID><DateRevised><Year>2020</Year><Month>04</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1538-7445</ISSN><JournalIssue CitedMedium="Internet"><Volume>79</Volume><Issue>7</Issue><PubDate><Year>2019</Year><Month>04</Month><Day>01</Day></PubDate></JournalIssue><Title>Cancer research</Title><ISOAbbreviation>Cancer Res.</ISOAbbreviation></Journal><ArticleTitle>Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue.</ArticleTitle><Pagination><MedlinePgn>1671-1680</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1158/0008-5472.CAN-18-2292</ELocationID><Abstract><AbstractText>Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRβ chains. To develop each classifier, we extracted 4-mers from every TCRβ CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays. SIGNIFICANCE: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.<i>See related commentary by Zoete and Coukos, p. 1299</i>.</AbstractText><CopyrightInformation>©2019 American Association for Cancer Research.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ostmeyer</LastName><ForeName>Jared</ForeName><Initials>J</Initials><Identifier Source="ORCID">0000-0003-0405-6987</Identifier><AffiliationInfo><Affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Christley</LastName><ForeName>Scott</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Toby</LastName><ForeName>Inimary T</ForeName><Initials>IT</Initials><AffiliationInfo><Affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cowell</LastName><ForeName>Lindsay G</ForeName><Initials>LG</Initials><AffiliationInfo><Affiliation>Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas. lindsay.cowell@utsouthwestern.edu.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 AI097403</GrantID><Acronym>AI</Acronym><Agency>NIAID NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>01</Month><Day>08</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Cancer Res</MedlineTA><NlmUniqueID>2984705R</NlmUniqueID><ISSNLinking>0008-5472</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D022801">Complementarity Determining Regions</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D011948">Receptors, Antigen, T-Cell</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D016693">Receptors, Antigen, T-Cell, alpha-beta</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="CommentIn"><RefSource>Cancer Res. 2019 Apr 1;79(7):1299-1301</RefSource><PMID Version="1">30936076</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D022801" MajorTopicYN="N">Complementarity Determining Regions</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016246" MajorTopicYN="Y">Lymphocytes, Tumor-Infiltrating</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011948" MajorTopicYN="N">Receptors, Antigen, T-Cell</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016693" MajorTopicYN="N">Receptors, Antigen, T-Cell, alpha-beta</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013601" MajorTopicYN="N">T-Lymphocytes</DescriptorName><QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>07</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2018</Year><Month>11</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>01</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>1</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>7</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>1</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">30622114</ArticleId><ArticleId IdType="pii">0008-5472.CAN-18-2292</ArticleId><ArticleId IdType="doi">10.1158/0008-5472.CAN-18-2292</ArticleId><ArticleId IdType="pmc">PMC6445742</ArticleId><ArticleId IdType="mid">NIHMS1518460</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Annu Rev Immunol. 2015;33:169-200</Citation><ArticleIdList><ArticleId IdType="pubmed">25493333</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Nucleic Acids Res. 2015 Jan;43(Database issue):D345-56</Citation><ArticleIdList><ArticleId IdType="pubmed">25428375</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Front Immunol. 2017 Apr 13;8:430</Citation><ArticleIdList><ArticleId IdType="pubmed">28450864</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>J Immunother Cancer. 2015 Jun 16;3:23</Citation><ArticleIdList><ArticleId IdType="pubmed">26085931</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Sci Rep. 2015 Sep 02;5:13664</Citation><ArticleIdList><ArticleId IdType="pubmed">26329277</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Nat Commun. 2013;4:2680</Citation><ArticleIdList><ArticleId IdType="pubmed">24157944</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Oncoimmunology. 2015 Jan 22;4(4):e1001230</Citation><ArticleIdList><ArticleId IdType="pubmed">26137399</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Cancer Immunol Immunother. 2013 Sep;62(9):1453-61</Citation><ArticleIdList><ArticleId IdType="pubmed">23771160</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Sci Rep. 2016 Jul 25;6:30064</Citation><ArticleIdList><ArticleId IdType="pubmed">27452728</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Sci Rep. 2018 Jan 18;8(1):1058</Citation><ArticleIdList><ArticleId IdType="pubmed">29348598</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>J Exp Med. 1998 Nov 2;188(9):1641-50</Citation><ArticleIdList><ArticleId IdType="pubmed">9802976</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Sci Rep. 2016 Oct 13;6:35326</Citation><ArticleIdList><ArticleId IdType="pubmed">27734930</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Mol Oncol. 2015 Dec;9(10):2063-70</Citation><ArticleIdList><ArticleId IdType="pubmed">26404496</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Clin Cancer Res. 2014 Sep 1;20(17):4540-8</Citation><ArticleIdList><ArticleId IdType="pubmed">24970842</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Nat Immunol. 2009 Feb;10(2):143-7</Citation><ArticleIdList><ArticleId IdType="pubmed">19148199</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Front Immunol. 2018 Mar 14;9:462</Citation><ArticleIdList><ArticleId IdType="pubmed">29593723</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Proc Natl Acad Sci U S A. 2005 May 3;102(18):6395-400</Citation><ArticleIdList><ArticleId IdType="pubmed">15851683</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>BMC Bioinformatics. 2017 Sep 7;18(1):401</Citation><ArticleIdList><ArticleId IdType="pubmed">28882107</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Science. 2018 Feb 23;359(6378):926-930</Citation><ArticleIdList><ArticleId IdType="pubmed">29348365</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Proc Natl Acad Sci U S A. 2016 Jul 19;113(29):7944-5</Citation><ArticleIdList><ArticleId IdType="pubmed">27410048</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>JAMA Oncol. 2015 Sep;1(6):746-54</Citation><ArticleIdList><ArticleId IdType="pubmed">26181891</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Nat Rev Immunol. 2008 Mar;8(3):231-8</Citation><ArticleIdList><ArticleId IdType="pubmed">18301425</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Oncotarget. 2017 Mar 28;8(13):21212-21228</Citation><ArticleIdList><ArticleId IdType="pubmed">28177902</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Annu Rev Immunol. 2000;18:495-527</Citation><ArticleIdList><ArticleId IdType="pubmed">10837067</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>EBioMedicine. 2015 Nov 24;2(12):2070-9</Citation><ArticleIdList><ArticleId IdType="pubmed">26844287</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Nat Med. 2014 May;20(5):548-54</Citation><ArticleIdList><ArticleId IdType="pubmed">24705333</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Immunity. 2002 Mar;16(3):345-54</Citation><ArticleIdList><ArticleId IdType="pubmed">11911820</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Genome Med. 2018 Mar 20;10(1):21</Citation><ArticleIdList><ArticleId IdType="pubmed">29558971</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Sci Transl Med. 2014 Feb 19;6(224):224ra24</Citation><ArticleIdList><ArticleId IdType="pubmed">24553385</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Proc Natl Acad Sci U S A. 2017 Nov 28;114(48):E10409-E10417</Citation><ArticleIdList><ArticleId IdType="pubmed">29138313</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Blood. 2015 Nov 26;126(22):2475-8</Citation><ArticleIdList><ArticleId IdType="pubmed">26468228</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>EBioMedicine. 2016 Aug;10:117-23</Citation><ArticleIdList><ArticleId IdType="pubmed">27377626</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Nat Genet. 2017 May;49(5):659-665</Citation><ArticleIdList><ArticleId IdType="pubmed">28369038</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Curr Opin Immunol. 2013 Apr;25(2):284-90</Citation><ArticleIdList><ArticleId IdType="pubmed">23566921</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Proc Natl Acad Sci U S A. 2016 Jul 19;113(29):8272-7</Citation><ArticleIdList><ArticleId IdType="pubmed">27307436</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Bioinformatics. 2014 Nov 15;30(22):3181-8</Citation><ArticleIdList><ArticleId IdType="pubmed">25095879</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Nature. 2017 Jan 18;541(7637):321-330</Citation><ArticleIdList><ArticleId IdType="pubmed">28102259</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Cancer Med. 2016 Sep;5(9):2513-21</Citation><ArticleIdList><ArticleId IdType="pubmed">27465739</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Proc Natl Acad Sci U S A. 2016 May 24;113(21):6005-10</Citation><ArticleIdList><ArticleId IdType="pubmed">27152024</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Oncoimmunology. 2013 Jul 1;2(7):e25205</Citation><ArticleIdList><ArticleId IdType="pubmed">24073380</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>PLoS One. 2016 Aug 11;11(8):e0160853</Citation><ArticleIdList><ArticleId IdType="pubmed">27513338</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Bioinformatics. 2017 Apr 1;33(7):951-955</Citation><ArticleIdList><ArticleId IdType="pubmed">28073756</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Sci Transl Med. 2013 Jan 9;5(167):167ra4</Citation><ArticleIdList><ArticleId IdType="pubmed">23303603</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Cancer Immunol Res. 2017 Feb;5(2):148-156</Citation><ArticleIdList><ArticleId IdType="pubmed">28039161</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Annu Rev Immunol. 2006;24:419-66</Citation><ArticleIdList><ArticleId IdType="pubmed">16551255</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Sci Rep. 2016 Apr 27;6:25070</Citation><ArticleIdList><ArticleId IdType="pubmed">27118724</ArticleId></ArticleIdList></Reference></ReferenceList><ReferenceList><Reference><Citation>Front Immunol. 2018 Feb 21;9:224</Citation><ArticleIdList><ArticleId IdType="pubmed">29515569</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Texas</li></region></list><tree><country name="États-Unis"><region name="Texas"><name sortKey="Ostmeyer, Jared" sort="Ostmeyer, Jared" uniqKey="Ostmeyer J" first="Jared" last="Ostmeyer">Jared Ostmeyer</name></region><name sortKey="Christley, Scott" sort="Christley, Scott" uniqKey="Christley S" first="Scott" last="Christley">Scott Christley</name><name sortKey="Cowell, Lindsay G" sort="Cowell, Lindsay G" uniqKey="Cowell L" first="Lindsay G" last="Cowell">Lindsay G. Cowell</name><name sortKey="Toby, Inimary T" sort="Toby, Inimary T" uniqKey="Toby I" first="Inimary T" last="Toby">Inimary T. Toby</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/MersV1/Data/PubMed/Checkpoint
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000653 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PubMed/Checkpoint/biblio.hfd -nk 000653 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= MersV1
|flux= PubMed
|étape= Checkpoint
|type= RBID
|clé= pubmed:30622114
|texte= Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/PubMed/Checkpoint/RBID.i -Sk "pubmed:30622114" \
| HfdSelect -Kh $EXPLOR_AREA/Data/PubMed/Checkpoint/biblio.hfd \
| NlmPubMed2Wicri -a MersV1
| This area was generated with Dilib version V0.6.33. |  |